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\begin{document}

\title{A Novel Spectral-Structural Analysis Method based on Corn Leaf
Hyperspectral Images for the Identification of Nitrogen Deficiencies}



\author[1]{Zhihang Song}%
\author[1]{Jinnuo Zhang}%
\author[1]{Xing Wei}%
\author[1]{Ziling Chen}%
\author[1]{Jian Jin}%
\affil[1]{Affiliation not available}%


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  \date{May 25, 2024}


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\begin{abstract}
As one of the largest supplied grain crops, corn plants often require a
significant amount of nitrogen fertilizer for optimal yield. However,
excessive fertilizer usage can lead to adverse environmental
consequences, especially for the nearby hydrological network. To
precisely manage nitrogen application, accurate measurement of corn crop
nitrogen deficiency is necessary. Hyperspectral imaging (HSI) techniques
are widely applied in plant phenotyping to effectively measure plant
traits caused by biotic or abiotic stresses. While previous HSI
processing methods primarily focus on the overall color change, they
rarely analyze the signal from the leaf-level spatial domain. However,
early-stage nitrogen deficiency symptoms may not significantly alter the
overall color, resulting in limited model performance in such cases. A
newly developed HSI device called LeafSpec can scan an entire corn leaf
with a high signal-over-noise ratio paired with high spatial-spectral
resolution, capturing the detailed color changes at the leaf structure
level. This study focused on identifying distinctive nitrogen deficiency
indicators using an innovative methodology that applies spectral
analysis to the details of leaf venation structures. The study started
with developing an automated venation segmentation algorithm to separate
a whole corn leaf into structural components. An in-depth examination of
the spectral profiles associated with different leaf components
introduced a new spatial-spectral index, demonstrating a higher
correlation with the nitrogen content data than the averaged spectral
indices. The high-resolution spectral-structural features discovered
with this method provided new potential to improve the performance of
the nitrogen prediction model in terms of both accuracy and robustness.%
\end{abstract}%



\sloppy


\emph{\textbf{Zhihang Song\textsuperscript{1}}, Jinnuo
Zhang\textsuperscript{\textbf{1}}, Xing Wei\textsuperscript{\textbf{1}},
Ziling Chen\textsuperscript{\textbf{1}}, Jian Jin\textsuperscript{1}}

\emph{\textsuperscript{1}Purdue University, West Lafayette, IN, United
States}

\textbf{ORCiD:} {[}0000-0003-2502-9937{]}

\textbf{Keywords:} Hyperspectral imaging, corn, nitrogen deficiency
prediction, spatial distribution analysis, leaf venation

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